The proposed system uses an algorithm that works on the admittance of the system,for estimating the reference values of generated currents for an off-grid wind power harnessing unit(WPHU).The controller controls the v...The proposed system uses an algorithm that works on the admittance of the system,for estimating the reference values of generated currents for an off-grid wind power harnessing unit(WPHU).The controller controls the voltage and maintains the frequency within the limits while working with both linear and nonlinear loads for varying wind speeds.The admittance algorithm is simple and easy to implement and works very efficiently to generate the triggering signals for the controller of the WPHU.The wind power harnessing unit comprising of a squirrel cage induction generator,a star-delta transformer,a battery storage system and the control unit are modeled using Matlab/Simulink R2019.An isolated transformer with a star-delta configuration connects the load and the generator circuit with the controller to reduce the dc bus voltage and mitigate current in the neutral line.The response of the system during the dynamic loading depends on the best possible compensator proportional-integral(PI)gains.The antlion optimization algorithm is compared with particle swarm optimization and grey wolf optimization and is found to have the advantages of good convergence,high efficiency and fast calculating speed.It is therefore used to extract the optimal values of frequency and voltage PI gains.The simulation results of the control algorithm for the WPHU are validated in a real-time environment in a dSpace1104 laboratory set up.This algorithm is proven to have a quick response,maintain the required frequency,suppress the current harmonics,regulate voltage,help in balancing the load and compensating for the neutral current.展开更多
This work proposes a novel nature-inspired algorithm called Ant Lion Optimizer (ALO). The ALO algorithm mimics the search mechanism of antlions in nature. A time domain based objective function is established to tune ...This work proposes a novel nature-inspired algorithm called Ant Lion Optimizer (ALO). The ALO algorithm mimics the search mechanism of antlions in nature. A time domain based objective function is established to tune the parameters of the PI controller based LFC, which is solved by the proposed ALO algorithm to reach the most convenient solutions. A three-area interconnected power system is investigated as a test system under various loading conditions to confirm the effectiveness of the suggested algorithm. Simulation results are given to show the enhanced performance of the developed ALO algorithm based controllers in comparison with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Bat Algorithm (BAT) and conventional PI controller. These results represent that the proposed BAT algorithm tuned PI controller offers better performance over other soft computing algorithms in conditions of settling times and several performance indices.展开更多
针对蚁狮优化算法(Ant Lion Optimization,ALO)后期收敛速度较慢和易陷入局部最优等问题,本文提出基于对数惯性权重的改进蚁狮优化算法(Logarithmic inertia weight based Ant Lion Optimization,LALO)。LALO利用对数函数的特点,实现对...针对蚁狮优化算法(Ant Lion Optimization,ALO)后期收敛速度较慢和易陷入局部最优等问题,本文提出基于对数惯性权重的改进蚁狮优化算法(Logarithmic inertia weight based Ant Lion Optimization,LALO)。LALO利用对数函数的特点,实现对惯性权重的非线性调整,从而更好地平衡算法的全局勘探和局部开采能力。同时,在算法的位置更新中,通过引入对数惯性权重策略来优化蚁狮个体的位置更新过程,降低算法陷入局部收敛的可能性,进而加快收敛速度。本文使用3个经典的测试函数来测试LALO的寻优性能。与已有的群智能算法相比,LALO加快了算法的收敛速度,提高了收敛精度和稳定性。展开更多
To overcome the limitations of traditional force aggregation methods,this paper proposes a novel clustering model integrating the self-adaptive tent chaos search ant lion optimizer(SATC-ALO)and the self-organizing map...To overcome the limitations of traditional force aggregation methods,this paper proposes a novel clustering model integrating the self-adaptive tent chaos search ant lion optimizer(SATC-ALO)and the self-organizing map(SOM)network.The model introduces a hybrid distance calculation method to measure inter-target distances and enhances the ant lion optimization algorithm through tent chaos sequences,adaptive tent chaos search,tournament selection,and logistic chaos sequences.Aggregation accuracy is evaluated using minimum quantization error and confidence value for the SOM neural network.The model is resolved using SATC-ALO and SOM independently,with experiments demonstrating that SOM achieves fast and accurate grouping,while SATC-ALO offers higher precision but requires longer computational runtime,making it more suitable for hybrid approaches.Both methods are validated as practical solutions for force aggregation tasks.展开更多
对于小电流接地系统的单相接地故障选线,传统方法普遍采用基于一维信号的选线模型,存在选线准确率低、抗噪性弱等问题。为此提出一种改进的变分模态分解及Conv Ne Xt的小电流接地系统单相接地故障选线方法。首先引入蚁狮算法优化变分模...对于小电流接地系统的单相接地故障选线,传统方法普遍采用基于一维信号的选线模型,存在选线准确率低、抗噪性弱等问题。为此提出一种改进的变分模态分解及Conv Ne Xt的小电流接地系统单相接地故障选线方法。首先引入蚁狮算法优化变分模态分解算法,通过蚁狮算法自动寻优选取合适的分解次数和惩罚因子,计算分解得到的各分量的分布熵,将其中的噪声分量筛选去除,将其余有效分量进行线性重构得到降噪后的零序电流信号;其次,将经过降噪处理后的一维零序电流信号经格拉姆角场转换为二维图像,制备故障选线数据集;然后,引入预训练的ConvNeXt模型,根据该研究数据模型特征,在其已有权重基础上对模型参数进行对应微调,从而提高模型精度并形成最终的选线模型;最后引入绝对平均误差、均方根误差作为评价指标验证所提降噪算法有效性。分别在加入噪声与否的前提下,将所提模型与3种选线模型相比较。实验结果表明该模型的准确率最高、抗噪性方面更好,其中该研究算法准确率达到了99.82%并且在不同噪声条件下都能维持91%以上的准确率,高于其他选线模型,克服了传统故障选线方法准确率低、抗噪性差的问题。展开更多
Aiming at the consumption problems caused by the high proportion of renewable energy being connected to the distribution network,it also aims to improve the power supply reliability of the power system and reduce the ...Aiming at the consumption problems caused by the high proportion of renewable energy being connected to the distribution network,it also aims to improve the power supply reliability of the power system and reduce the operating costs of the power system.This paper proposes a two-stage planning method for distributed generation and energy storage systems that considers the hierarchical partitioning of source-storage-load.Firstly,an electrical distance structural index that comprehensively considers active power output and reactive power output is proposed to divide the distributed generation voltage regulation domain and determine the access location and number of distributed power sources.Secondly,a two-stage planning is carried out based on the zoning results.In the phase 1 distribution network-zoning optimization layer,the network loss is minimized so that the node voltage in the area does not exceed the limit,and the distributed generation configuration results are initially determined;in phase 2,the partition-node optimization layer is planned with the goal of economic optimization,and the distance-based improved ant lion algorithm is used to solve the problem to obtain the optimal distributed generation and energy storage systemconfiguration.Finally,the IEEE33 node systemwas used for simulation.The results showed that the voltage quality was significantly improved after optimization,and the overall revenue increased by about 20.6%,verifying the effectiveness of the two-stage planning.展开更多
The new energy industry is strongly supported by the state,and accurate forecasting of stock price can lead to better understanding of its development.However,factors such as cost and ease of use of new energy,as well...The new energy industry is strongly supported by the state,and accurate forecasting of stock price can lead to better understanding of its development.However,factors such as cost and ease of use of new energy,as well as economic situation and policy environment,have led to continuous changes in its stock price and increased stock price volatility.By calculating the Lyapunov index and observing the Poincarésurface of the section,we find that the sample of the China Securities Index Green Power 50 Index has chaotic characteristics,and the data indicate strong volatility and uncertainty.This study proposes a new method of stock price index prediction,namely,EWT-S-ALOSVR.Empirical wavelet decomposition extracts features from multiple factors affecting stock prices to form multiple sub-columns with features,significantly reducing the complexity of the stock price series.Support vector regression is well suited for dealing with nonlinear stock price series,and the support vector machine model parameters are selected using random wandering and picking elites via Ant Lion Optimization,making stock price prediction more accurate.展开更多
摘要The proposed system uses an algorithm that works on the admittance of the system,for estimating the reference values of generated currents for an off-grid wind power harnessing unit(WPHU).The controller controls the voltage and maintains the frequency within the limits while working with both linear and nonlinear loads for varying wind speeds.The admittance algorithm is simple and easy to implement and works very efficiently to generate the triggering signals for the controller of the WPHU.The wind power harnessing unit comprising of a squirrel cage induction generator,a star-delta transformer,a battery storage system and the control unit are modeled using Matlab/Simulink R2019.An isolated transformer with a star-delta configuration connects the load and the generator circuit with the controller to reduce the dc bus voltage and mitigate current in the neutral line.The response of the system during the dynamic loading depends on the best possible compensator proportional-integral(PI)gains.The antlion optimization algorithm is compared with particle swarm optimization and grey wolf optimization and is found to have the advantages of good convergence,high efficiency and fast calculating speed.It is therefore used to extract the optimal values of frequency and voltage PI gains.The simulation results of the control algorithm for the WPHU are validated in a real-time environment in a dSpace1104 laboratory set up.This algorithm is proven to have a quick response,maintain the required frequency,suppress the current harmonics,regulate voltage,help in balancing the load and compensating for the neutral current.
摘要This work proposes a novel nature-inspired algorithm called Ant Lion Optimizer (ALO). The ALO algorithm mimics the search mechanism of antlions in nature. A time domain based objective function is established to tune the parameters of the PI controller based LFC, which is solved by the proposed ALO algorithm to reach the most convenient solutions. A three-area interconnected power system is investigated as a test system under various loading conditions to confirm the effectiveness of the suggested algorithm. Simulation results are given to show the enhanced performance of the developed ALO algorithm based controllers in comparison with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Bat Algorithm (BAT) and conventional PI controller. These results represent that the proposed BAT algorithm tuned PI controller offers better performance over other soft computing algorithms in conditions of settling times and several performance indices.
摘要针对蚁狮优化算法(Ant Lion Optimization,ALO)后期收敛速度较慢和易陷入局部最优等问题,本文提出基于对数惯性权重的改进蚁狮优化算法(Logarithmic inertia weight based Ant Lion Optimization,LALO)。LALO利用对数函数的特点,实现对惯性权重的非线性调整,从而更好地平衡算法的全局勘探和局部开采能力。同时,在算法的位置更新中,通过引入对数惯性权重策略来优化蚁狮个体的位置更新过程,降低算法陷入局部收敛的可能性,进而加快收敛速度。本文使用3个经典的测试函数来测试LALO的寻优性能。与已有的群智能算法相比,LALO加快了算法的收敛速度,提高了收敛精度和稳定性。
基金supported by the Youth Talent Support Program of Xi’an Association for Science and Technology(0959202513098)the National Natural Science Foundation of China(62106284)the Natural Science Foundation of Shanxi Province(2021JQ370).
摘要To overcome the limitations of traditional force aggregation methods,this paper proposes a novel clustering model integrating the self-adaptive tent chaos search ant lion optimizer(SATC-ALO)and the self-organizing map(SOM)network.The model introduces a hybrid distance calculation method to measure inter-target distances and enhances the ant lion optimization algorithm through tent chaos sequences,adaptive tent chaos search,tournament selection,and logistic chaos sequences.Aggregation accuracy is evaluated using minimum quantization error and confidence value for the SOM neural network.The model is resolved using SATC-ALO and SOM independently,with experiments demonstrating that SOM achieves fast and accurate grouping,while SATC-ALO offers higher precision but requires longer computational runtime,making it more suitable for hybrid approaches.Both methods are validated as practical solutions for force aggregation tasks.
摘要对于小电流接地系统的单相接地故障选线,传统方法普遍采用基于一维信号的选线模型,存在选线准确率低、抗噪性弱等问题。为此提出一种改进的变分模态分解及Conv Ne Xt的小电流接地系统单相接地故障选线方法。首先引入蚁狮算法优化变分模态分解算法,通过蚁狮算法自动寻优选取合适的分解次数和惩罚因子,计算分解得到的各分量的分布熵,将其中的噪声分量筛选去除,将其余有效分量进行线性重构得到降噪后的零序电流信号;其次,将经过降噪处理后的一维零序电流信号经格拉姆角场转换为二维图像,制备故障选线数据集;然后,引入预训练的ConvNeXt模型,根据该研究数据模型特征,在其已有权重基础上对模型参数进行对应微调,从而提高模型精度并形成最终的选线模型;最后引入绝对平均误差、均方根误差作为评价指标验证所提降噪算法有效性。分别在加入噪声与否的前提下,将所提模型与3种选线模型相比较。实验结果表明该模型的准确率最高、抗噪性方面更好,其中该研究算法准确率达到了99.82%并且在不同噪声条件下都能维持91%以上的准确率,高于其他选线模型,克服了传统故障选线方法准确率低、抗噪性差的问题。
基金supported by North China Electric Power Research Institute’s Self-Funded Science and Technology Project“Research on Distributed Energy Storage Optimal Configuration and Operation Control Technology for Photovoltaic Promotion in the Entire County”(KJZ2022049).
摘要Aiming at the consumption problems caused by the high proportion of renewable energy being connected to the distribution network,it also aims to improve the power supply reliability of the power system and reduce the operating costs of the power system.This paper proposes a two-stage planning method for distributed generation and energy storage systems that considers the hierarchical partitioning of source-storage-load.Firstly,an electrical distance structural index that comprehensively considers active power output and reactive power output is proposed to divide the distributed generation voltage regulation domain and determine the access location and number of distributed power sources.Secondly,a two-stage planning is carried out based on the zoning results.In the phase 1 distribution network-zoning optimization layer,the network loss is minimized so that the node voltage in the area does not exceed the limit,and the distributed generation configuration results are initially determined;in phase 2,the partition-node optimization layer is planned with the goal of economic optimization,and the distance-based improved ant lion algorithm is used to solve the problem to obtain the optimal distributed generation and energy storage systemconfiguration.Finally,the IEEE33 node systemwas used for simulation.The results showed that the voltage quality was significantly improved after optimization,and the overall revenue increased by about 20.6%,verifying the effectiveness of the two-stage planning.
基金Key Research Project in Universities of Henan Province(No.24B480012)Science and Technology of Henan Province of China(No.182400410419)+1 种基金the Foundation for Fostering the National Foundation of Pingdingshan University(No.PXY-PYJJ-2016006)National Science and Technology Council,Taiwan(MOST 111-2410-H-161-001).
摘要The new energy industry is strongly supported by the state,and accurate forecasting of stock price can lead to better understanding of its development.However,factors such as cost and ease of use of new energy,as well as economic situation and policy environment,have led to continuous changes in its stock price and increased stock price volatility.By calculating the Lyapunov index and observing the Poincarésurface of the section,we find that the sample of the China Securities Index Green Power 50 Index has chaotic characteristics,and the data indicate strong volatility and uncertainty.This study proposes a new method of stock price index prediction,namely,EWT-S-ALOSVR.Empirical wavelet decomposition extracts features from multiple factors affecting stock prices to form multiple sub-columns with features,significantly reducing the complexity of the stock price series.Support vector regression is well suited for dealing with nonlinear stock price series,and the support vector machine model parameters are selected using random wandering and picking elites via Ant Lion Optimization,making stock price prediction more accurate.